| import streamlit as st |
| import tensorflow as tf |
| import tensorflow_hub as hub |
| import numpy as np |
| from PIL import Image |
|
|
| st.write("Loading model...") |
| module = hub.load("https://tfhub.dev/google/progan-128/1") |
| def generate_random_face(): |
| latent_vector = np.random.normal(size=[1,512]).astype(np.float32) |
| image = module.signatures['default'](tf.constant(latent_vector))['default'] |
| image = np.uint8(image.numpy()[0]*255) |
| return Image.fromarray(image) |
|
|
| st.title("Random Face Generator") |
| st.write("Click the button below to generate a random AI generated face") |
|
|
| if st.button("Generate Random Face"): |
| generated_image = generate_random_face() |
| st.image(generated_image, caption="Generated Face", use_container_width=True) |
| image_array = np.array(generated_image) |
| txt_filename = "generated_face.txt" |
| np.savetxt(txt_filename,image_array.flatten(), fmt="%d") |
| st.download_button("Download Image as TXT", txt_filename, txt_filename, "text/plain") |
| st.sidebar.write("App Version: 1.0") |
| st.sidebar.write("Model: ProGAN-128 from Tensorflow Hub") |
